Hire Data Engineers

At KPS, when we help you hire a data engineer, we evaluate more than just a resume. Our process involves deep, context-based interviews with each data engineer to understand their technical expertise and how they’ve approached real-world data challenges, like:

• handling dirty data

• migrating from legacy pipelines

• building something scalable from scratch

We ask them to walk us through actual projects they’ve worked on, how they made decisions, and what they’d do differently today.

Technical skills and knowledge alone aren't enough. We also assess their communication skills, ability to collaborate with product and analytics teams, and how they troubleshoot in live environments. A senior title means little if the person can’t adapt or explain their logic. That’s why our vetting filters out candidates who only look good on paper but lack the ability to contribute meaningfully.

Finally, we consider the context of your project. An ideal data engineer might be excellent with Spark but not the right fit for a real-time architecture. Or someone might have strong ETL skills but little experience with data modeling. Our goal is to match experience not just to a job title, but to your system and business reality.

Our top data engineers work across modern stacks and data processing frameworks and are comfortable switching contexts when needed:

• On the data processing side, they regularly use data engineering tools like Apache Spark, Airflow, dbt, Kafka, and Flink. 

• For data storage and querying, you’ll find engineers fluent in BigQuery, Snowflake, Redshift, PostgreSQL, and NoSQL databases (for structured data and unstructured data collections) like DynamoDB or MongoDB.

We also have engineers who specialize in data transformation, data build tools, data processing tasks, streaming, or batch processing, depending on the system load and latency expectations (if you want to hire big data engineers who also know data science).

When it comes to programming languages, Python is the go-to for scripting and ETL, while SQL expertise is a must across the board. Some engineers also work with Scala or Java, especially for distributed data environments. And when the data needs to be exposed or served, our engineers can integrate APIs, set up data access layers, and handle authentication layers securely.

In terms of cloud, we cover:

• AWS (Redshift, Glue, S3, EMR)

• GCP (BigQuery, Dataflow)

• Azure (Synapse, Data Factory)

You won’t have to worry about hiring someone who’s never touched your stack; we make sure their experience lines up with your environment from the start.

Our experienced data engineers come from a wide range of industries, and we treat this as a strength. Many have worked in e-commerce and retail, building data warehouses for tracking inventory and customer behavior. Others come from FinTech, where they’ve handled strict compliance requirements and real-time fraud detection pipelines. Healthcare is another frequent domain where engineers have worked with sensitive patient data and HIPAA-compliant systems.

We also support SaaS and B2B platforms, where engineers deal with product usage data, churn prediction models, and subscription analytics. Some of our most technically challenging projects have come from logistics and mobility, where telemetry and location data must be processed in near real-time. Regardless of the industry, we prioritize engineers who’ve had to adapt quickly, build clean data foundations, and support analysts and product teams at scale.

If your industry has niche data structures or compliance rules, we’ll only match you with engineers who’ve seen that terrain before and can bring in patterns that already work.

Our engineers are trained to listen before they build. During the onboarding phase, they don’t just ask about data sources; they ask about the reasons, consequences, and your data engineering needs:

• What metrics does the business rely on? 

• Who is consuming the data? 

• What decisions are blocked because of current limitations? 

These questions guide how the system is designed.

We also encourage engineers to speak directly with analysts, stakeholders, or whoever is closest to the business goals. This helps avoid classic problems like building a dashboard that no one uses or a model that doesn’t reflect reality. Our engineers see their job as enabling business impact, not just transforming raw files.

Once the work begins, they continue to validate: sharing progress early, documenting assumptions, and clarifying business logic. This kind of back-and-forth ensures the data architecture actually supports the decisions people are trying to make, not just looks clean in a diagram.

Skilled data engineers don’t just move data, they unlock insights. When our engineers join a project, they work backward from the key business outcomes. That could mean:

• improving customer retention

• supporting faster product iterations

• reducing manual reporting hours

Once the goal is clear, they design pipelines and data models, data lakes that directly support those outcomes.

They also partner closely with data analysts and scientists to make sure the data is structured in ways that are usable, consistent, and fast to query. That might include:

• building reusable data marts

• implementing naming standards

• setting up data quality monitoring that prevents broken dashboards

The idea is to make the entire data layer more trusted, so teams spend less time guessing and more time deciding.

Finally, they don’t disappear once something is shipped. Our engineers stay engaged to help fine-tune performance, add new sources, or support evolving KPIs. That level of continuity is key when business goals shift, and why clients rely on our engineers long after the first scope is delivered.